CC BY 4.0Kurzidem, IwoIwoKurzidemBurton, SimonSimonBurtonSchleiß, PhilippPhilippSchleiß2023-11-132023-11-132023https://publica.fraunhofer.de/handle/publica/456788https://doi.org/10.24406/publica-214710.24406/publica-2147Current research in machine learning (ML) and safety focuses on safety assurance of ML. We, however, show how to interpret results of explainable ML approaches for safety. We investigate how individual evaluation of data clusters in specific explainable, outside-model estimators can be analyzed to identify insufficiencies at different levels, such as (1) input feature, (2) data or (3) the ML model itself. Additionally, we link our finding to required artifacts of safety within the automotive domain, such as unknown unknowns from ISO 21448 or equivalence class as mentioned in ISO/TR 4804. In our case study we analyze and evaluate the results from an explainable, outside-model estimator (i.e., white-box model) by performance evaluation, decision tree visualization, data distribution and input feature correlation. As explainability is key for safety analyses, the utilized model is a random forest, with extensions via boosting and multi-output regression. The model training is based on an introspective data set, optimized for reliable safety estimation. Our results show that technical limitations can be identified via homogeneous data clusters and assigned to a corresponding equivalence class. For unknown unknowns, each level of insufficiency (input, data and model) must be analyzed separately and systematically narrowed down by process of elimination. In our case study we identify "Fog density" as an unknown unknown input feature for the introspective model.ensafety analysissafety engineeringexplainable machine learningoutside-model estimatorsafety validationconference paper